Focusing on the issue that traditional pooling methods cannot extract valid eigenvalues, an adaptive pooling method was proposed to adjust the pooling results based on the size of the pooling domain, the element values in the pooling domain and the network training rounds. A function based on the interpolation principle and the maximum pooling model was constructed, and the specific function value was used as the pooling result. Then the cross-validation method was used for the comparative experiment of the model. Focusing on the issue that the hyperparameter selection based on empirical values to verify on all datasets is inefficient, a small sample tuning method was proposed. On the original data set, small samples were extracted according to the rule of stratified sampling, and the encoded hyperparameter combinations were cyclically trained and tested based on the small data set. The optimal hyperparameters were identified by decoding the combination with the highest recognition. Using DeepFashion dataset for the related experiments, the results show that the recognition rate of adaptive pooling model is about 83%, which is about 2.5% higher than that of the maximum pooling model. Hyperparameters selected by small samples were compared though experiments with random combinations of hyperparameters on the original dataset. The results show that the hyperparameters selected by the small sample tuning method are optimal within the empirical value range. The recognition result is 86.98%, which is about 41.4% higher than the average recognition rate of the hyperparameters random combinations. The adaptive pooling method can be extended to other neural networks, and the small sample tuning method provides a basis for efficient selection of hyperparameters of deep neural networks.